Feedforward neural networks for principal components extraction.

DOI10.1016/S0167-9473(99)00063-8zbMath1061.62560OpenAlexW1985690866MaRDI QIDQ1575401

Sandro Nicole

Publication date: 21 August 2000

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(99)00063-8

zbMATH Keywords

Principal componentsEigenvectorsNeural nets

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Neural nets and related approaches to inference from stochastic processes (62M45) Applications of statistics to psychology (62P15)

Related Items (3)

Comparison of the performance of multi-layer perceptron and linear regression for epidemiological data ⋮ Two-phase incremental kernel PCA for learning massive or online datasets ⋮ Computational and space complexity analysis of SubXPCA

Cites Work

• Self-organization and associative memory
• Auto-association by multilayer perceptrons and singular value decomposition
• A simplified neuron model as a principal component analyzer
• A network model with auto-oscillating output and dynamic connections
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